app.py

import copy
import functools
import itertools
import logging
import random
import string
from typing import List, Optional

import requests
import numpy as np
import tensorflow as tf
import streamlit as st
from gazpacho import Soup, get
from transformers import BertTokenizer, TFAutoModelForMaskedLM
from rhyme_with_ai.utils import color_new_words, pairwise, find_last_word, sanitize


DEFAULT_QUERY = "Machines will take over the world soon"
N_RHYMES = 10
ITER_FACTOR = 5


LANGUAGE = st.sidebar.radio("Language", ["english", "dutch"],0)
if LANGUAGE == "english":
    MODEL_PATH = "bert-large-cased-whole-word-masking"
elif LANGUAGE == "dutch":
    MODEL_PATH = "GroNLP/bert-base-dutch-cased"
else:
    raise NotImplementedError(f"Unsupported language ({LANGUAGE}) expected 'english' or 'dutch'.")

def main():
    st.markdown(
        "<sup>Created with "
        "[Datamuse](https://www.datamuse.com/api/), "
        "[Mick's rijmwoordenboek](https://rijmwoordenboek.nl), "
        "[Hugging Face](https://huggingface.co/), "
        "[Streamlit](https://streamlit.io/) and "
        "[App Engine](https://cloud.google.com/appengine/)."
        " Read our [blog](https://blog.godatadriven.com/rhyme-with-ai) "
        "or check the "
        "[source](https://github.com/godatadriven/rhyme-with-ai).</sup>",
        unsafe_allow_html=True,
    )
    st.title("Rhyme with AI")
    query = get_query()
    if not query:
        query = DEFAULT_QUERY
    rhyme_words_options = query_rhyme_words(query, n_rhymes=N_RHYMES,language=LANGUAGE)
    if rhyme_words_options:
        logging.getLogger(__name__).info("Got rhyme words: %s", rhyme_words_options)
        start_rhyming(query, rhyme_words_options)
    else:
        st.write("No rhyme words found")


def get_query():
    q = sanitize(
        st.text_input("Write your first line and press ENTER to rhyme:", DEFAULT_QUERY)
    )
    if not q:
        return DEFAULT_QUERY
    return q


def start_rhyming(query, rhyme_words_options):
    st.markdown("## My Suggestions:")

    progress_bar = st.progress(0)
    status_text = st.empty()
    max_iter = len(query.split()) * ITER_FACTOR

    rhyme_words = rhyme_words_options[:N_RHYMES]

    model, tokenizer = load_model(MODEL_PATH)
    sentence_generator = RhymeGenerator(model, tokenizer)
    sentence_generator.start(query, rhyme_words)

    current_sentences = [" " for _ in range(N_RHYMES)]
    for i in range(max_iter):
        previous_sentences = copy.deepcopy(current_sentences)
        current_sentences = sentence_generator.mutate()
        display_output(status_text, query, current_sentences, previous_sentences)
        progress_bar.progress(i / (max_iter - 1))
    st.balloons()


@st.cache(allow_output_mutation=True)
def load_model(model_path):
    return (
        TFAutoModelForMaskedLM.from_pretrained(model_path),
        BertTokenizer.from_pretrained(model_path),
    )


def display_output(status_text, query, current_sentences, previous_sentences):
    print_sentences = []
    for new, old in zip(current_sentences, previous_sentences):
        formatted = color_new_words(new, old)
        after_comma = "<li>" + formatted.split(",")[1][:-2] + "</li>"
        print_sentences.append(after_comma)
    status_text.markdown(
        query + ",<br>" + "".join(print_sentences), unsafe_allow_html=True
    )

class TokenWeighter:
    def __init__(self, tokenizer):
        self.tokenizer_ = tokenizer
        self.proba = self.get_token_proba()

    def get_token_proba(self):
        valid_token_mask = self._filter_short_partial(self.tokenizer_.vocab)
        return valid_token_mask

    def _filter_short_partial(self, vocab):
        valid_token_ids = [v for k, v in vocab.items() if len(k) > 1 and "#" not in k]
        is_valid = np.zeros(len(vocab.keys()))
        is_valid[valid_token_ids] = 1
        return is_valid


class RhymeGenerator:
    def __init__(
        self,
        model: TFAutoModelForMaskedLM,
        tokenizer: BertTokenizer,
        token_weighter: TokenWeighter = None,
    ):
        """Generate rhymes.

        Parameters
        ----------
        model : Model for masked language modelling
        tokenizer : Tokenizer for model
        token_weighter : Class that weighs tokens
        """

        self.model = model
        self.tokenizer = tokenizer
        if token_weighter is None:
            token_weighter = TokenWeighter(tokenizer)
        self.token_weighter = token_weighter
        self._logger = logging.getLogger(__name__)

        self.tokenized_rhymes_ = None
        self.position_probas_ = None

        # Easy access.
        self.comma_token_id = self.tokenizer.encode(",", add_special_tokens=False)[0]
        self.period_token_id = self.tokenizer.encode(".", add_special_tokens=False)[0]
        self.mask_token_id = self.tokenizer.mask_token_id

    def start(self, query: str, rhyme_words: List[str]) -> None:
        """Start the sentence generator.

        Parameters
        ----------
        query : Seed sentence
        rhyme_words : Rhyme words for next sentence
        """
        # TODO: What if no content?
        self._logger.info("Got sentence %s", query)
        tokenized_rhymes = [
            self._initialize_rhymes(query, rhyme_word) for rhyme_word in rhyme_words
        ]
        # Make same length.
        self.tokenized_rhymes_ = tf.keras.preprocessing.sequence.pad_sequences(
            tokenized_rhymes, padding="post", value=self.tokenizer.pad_token_id
        )
        p = self.tokenized_rhymes_ == self.tokenizer.mask_token_id
        self.position_probas_ = p / p.sum(1).reshape(-1, 1)

    def _initialize_rhymes(self, query: str, rhyme_word: str) -> List[int]:
        """Initialize the rhymes.

        * Tokenize input
        * Append a comma if the sentence does not end in it (might add better predictions as it
            shows the two sentence parts are related)
        * Make second line as long as the original
        * Add a period

        Parameters
        ----------
        query : First line
        rhyme_word : Last word for second line

        Returns
        -------
        Tokenized rhyme lines
        """

        query_token_ids = self.tokenizer.encode(query, add_special_tokens=False)
        rhyme_word_token_ids = self.tokenizer.encode(
            rhyme_word, add_special_tokens=False
        )

        if query_token_ids[-1] != self.comma_token_id:
            query_token_ids.append(self.comma_token_id)

        magic_correction = len(rhyme_word_token_ids) + 1  # 1 for comma
        return (
            query_token_ids
            + [self.tokenizer.mask_token_id] * (len(query_token_ids) - magic_correction)
            + rhyme_word_token_ids
            + [self.period_token_id]
        )

    def mutate(self):
        """Mutate the current rhymes.

        Returns
        -------
        Mutated rhymes
        """
        self.tokenized_rhymes_ = self._mutate(
            self.tokenized_rhymes_, self.position_probas_, self.token_weighter.proba
        )

        rhymes = []
        for i in range(len(self.tokenized_rhymes_)):
            rhymes.append(
                self.tokenizer.convert_tokens_to_string(
                    self.tokenizer.convert_ids_to_tokens(
                        self.tokenized_rhymes_[i], skip_special_tokens=True
                    )
                )
            )
        return rhymes

    def _mutate(
        self,
        tokenized_rhymes: np.ndarray,
        position_probas: np.ndarray,
        token_id_probas: np.ndarray,
    ) -> np.ndarray:

        replacements = []
        for i in range(tokenized_rhymes.shape[0]):
            mask_idx, masked_token_ids = self._mask_token(
                tokenized_rhymes[i], position_probas[i]
            )
            tokenized_rhymes[i] = masked_token_ids
            replacements.append(mask_idx)

        predictions = self._predict_masked_tokens(tokenized_rhymes)

        for i, token_ids in enumerate(tokenized_rhymes):
            replace_ix = replacements[i]
            token_ids[replace_ix] = self._draw_replacement(
                predictions[i], token_id_probas, replace_ix
            )
            tokenized_rhymes[i] = token_ids

        return tokenized_rhymes

    def _mask_token(self, token_ids, position_probas):
        """Mask line and return index to update."""
        token_ids = self._mask_repeats(token_ids, position_probas)
        ix = self._locate_mask(token_ids, position_probas)
        token_ids[ix] = self.mask_token_id
        return ix, token_ids

    def _locate_mask(self, token_ids, position_probas):
        """Update masks or a random token."""
        if self.mask_token_id in token_ids:
            # Already masks present, just return the last.
            # We used to return thee first but this returns worse predictions.
            return np.where(token_ids == self.tokenizer.mask_token_id)[0][-1]
        return np.random.choice(range(len(position_probas)), p=position_probas)

    def _mask_repeats(self, token_ids, position_probas):
        """Repeated tokens are generally of less quality."""
        repeats = [
            ii for ii, ids in enumerate(pairwise(token_ids[:-2])) if ids[0] == ids[1]
        ]
        for ii in repeats:
            if position_probas[ii] > 0:
                token_ids[ii] = self.mask_token_id
            if position_probas[ii + 1] > 0:
                token_ids[ii + 1] = self.mask_token_id
        return token_ids

    def _predict_masked_tokens(self, tokenized_rhymes):
        return self.model(tf.constant(tokenized_rhymes))[0]

    def _draw_replacement(self, predictions, token_probas, replace_ix):
        """Get probability, weigh and draw."""
        # TODO (HG): Can't we softmax when calling the model?
        probas = tf.nn.softmax(predictions[replace_ix]).numpy() * token_probas
        probas /= probas.sum()
        return np.random.choice(range(len(probas)), p=probas)



def query_rhyme_words(sentence: str, n_rhymes: int, language:str="english") -> List[str]:
    """Returns a list of rhyme words for a sentence.

    Parameters
    ----------
    sentence : Sentence that may end with punctuation
    n_rhymes : Maximum number of rhymes to return

    Returns
    -------
        List[str] -- List of words that rhyme with the final word
    """
    last_word = find_last_word(sentence)
    if language == "english":
       return query_datamuse_api(last_word, n_rhymes)
    elif language == "dutch":
        return mick_rijmwoordenboek(last_word, n_rhymes)
    else:
        raise NotImplementedError(f"Unsupported language ({language}) expected 'english' or 'dutch'.")


def query_datamuse_api(word: str, n_rhymes: Optional[int] = None) -> List[str]:
    """Query the DataMuse API.

    Parameters
    ----------
    word : Word to rhyme with
    n_rhymes : Max rhymes to return

    Returns
    -------
    Rhyme words
    """
    out = requests.get(
        "https://api.datamuse.com/words", params={"rel_rhy": word}
    ).json()
    words = [_["word"] for _ in out]
    if n_rhymes is None:
        return words
    return words[:n_rhymes]


@functools.lru_cache(maxsize=128, typed=False)
def mick_rijmwoordenboek(word: str, n_words: int):
    url = f"https://rijmwoordenboek.nl/rijm/{word}"
    html = get(url)
    soup = Soup(html)

    results = soup.find("div", {"id": "rhymeResultsWords"}).html.split("<br>")

    # clean up
    results = [r.replace("\n", "").replace(" ", "") for r in results]

    # filter html and empty strings
    results = [r for r in results if ("<" not in r) and (len(r) > 0)]

    return random.sample(results, min(len(results), n_words))


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    main()